Structural Transformation of the Agricultural Sector and Influencing
Factors in Sumatra Island
DWI HARYONO1, VINNI AURELIA SALSABILA1, TEGUH ENDARYANTO1, MUHAMMAD
IRFAN AFFANDI1, FIRDASARI1
Agribusiness Departement, Universitas Lampung, Bandar Lampung, INDONESIA
Abstract: - The objective of this research is to determine whether there has been a transformation in Sumatra
Island during the period from 2010 to 2022 and to identify the influencing factors. The data utilized in this
study is secondary data obtained from official sources such as the Central Statistics Agency. The collected data
includes Gross Regional Domestic Product (GRDP), poverty rate, open unemployment rate, investment data,
and average years of schooling. The findings reveal that over the 12-year period, Sumatra Island did not
experience a significant transformation, despite the agricultural sector being its primary contributor. The island
only underwent a shift of 0.62, a value smaller than that of the mining sector. The factors influencing the shift
in the agricultural sector in Sumatra Island include the unemployment rate, poverty rate, investment, and
average years of schooling.
Key-Words: - Transformation, Economic Development, Agricultural Sector, Influencing Factors
Received: June 24, 2023. Revised: April 6, 2024. Accepted: May 26, 2024. Published: June 28, 2024.
1 Introduction
The abundant diversity in Indonesia creates varied
potentials in each region. These differences arise
due to the distinct characteristics of each region,
leading to a possibility of leaning towards a specific
aspect with the greatest potential in that area [1].
This results in varying economic conditions in each
region. Developing countries like Indonesia
typically initiate regional development, beginning
with the economic aspect, as it is considered crucial
and functions to meet societal needs. Economic
development can support goal achievement and
drive innovation in other aspects and sectors [2].
One indicator that can illustrate the differences in
economic conditions across regions in Indonesia is
the Gross Regional Domestic Product (GRDP).
Based on the published GRDP data by the Central
Statistics Agency (Badan Pusat Statistik), it is
known that the largest contribution comes from Java
Island at 58.69 percent, followed by Sumatra Island
at 21 percent, Kalimantan Island at 8.21 percent,
Sulawesi Island at 6.73 percent, Bali Island at 2.75
percent, and lastly, Papua and Maluku Islands at
2.61 percent [3].
Sumatra Island is one of the largest islands in
Indonesia, covering approximately 443,065.8 km2,
and it is the second-fastest-growing economy after
Java Island. Sumatra Island is notable for its
agricultural sector. According to the 2021 data from
the Central Statistics Agency (BPS), Sumatra Island
excels in its plantation sub-sector, making it a
dominant force in this sub-sector. The produced
plantation commodities include palm oil, rubber,
coconut, coffee, and betel nut. The plantation sector,
particularly oil palm plantations, remains a key
player in Sumatra Island, contributing 53 percent of
the national palm oil production, equivalent to 24.4
million tons in 2021. In other words, half of the
national palm oil production originates from
Sumatra Island. The robust economic activities are
supported by the abundant natural resources in the
region, particularly in the agricultural sector [4].
The largest contribution to Sumatra Island's GRDP
is from the agricultural sector, accounting for 23.35
percent in 2020. This figure represents an increase
compared to the previous years, which were 17.80
percent in 2010 and 23.16 percent in 2015.
However, there was a significant increase from 2010
to 2015, amounting to 5.36 percent, whereas from
2015 to 2020, within the same time frame of five
years, the contribution increased by only 0.19
percent. The comparison of increases within the
same time frame differs significantly. Other sectors
experiencing growth include the trade sector and the
accommodation and food service activities sector.
Conversely, the manufacturing industry and mining
and quarrying sector experienced a considerable
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.17
Dwi Haryono, Vinni Aurelia Salsabila,
Teguh Endaryanto, Muhammad Irfan Affandi, Firdasari
E-ISSN: 2945-1159
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decline in the last decade. The overall increase in
contribution in Sumatra Island suggests that the
agricultural sector still holds potential for effective
utilization in regional development. However, this
increase is not evenly distributed across all
provinces in Sumatra Island.
As a sector that tends to have a significant
contribution to the primary sector, Sumatra Island
serves as a resource-rich hub with the potential to
develop value-added products through the
enhancement of the secondary sector, especially the
manufacturing industry, as part of the
transformation and industrialization process.
Industrialization is a modernization process that
encompasses all economic sectors interconnected
with the manufacturing industry, aiming to generate
added value. Therefore, with industrial
development, it will stimulate and uplift other
sectors [5]. Hence, the aim of this research is to
determine whether Sumatra Island has undergone
transformation with its abundant agricultural
resources and identify the factors influencing this
transformation.
2 Research Method
2.1 Data Collection
The data utilized in this research consists of
secondary data on the GRDP at constant prices
(ADHK) of Sumatra Island from the years 2010 to
2022. Secondary data refers to information that is
not directly provided to the data collector but is
obtained through intermediaries or documents [6].
The method for collecting secondary data involves
gathering information from available sources such
as documents, publications, databases, archives, and
other officially published or publicized sources [7].
The research method applied in this study is the case
study method [8]. In addition to the GRDP data for
the years 2010-2022 [9], other data used include the
Human Development Index, the number of people
living in poverty, the unemployment rate, and the
economic growth rate sourced from the Badan Pusat
Statistik (BPS).
2.2 Data Analysis
2.2.1 Metode Analisis Deskriptif
This analysis is used to examine the transformation
of the economic structure in Sumatra Island [10].
Microsoft Excel is employed for this analysis. The
discussed outcomes of this analysis include the
economic sector conditions in Sumatra Island,
showcasing the fluctuations in GRDP contributions
with a research sample from the years 2010 to 2022.
2.2.2 Analisis Data Panel
This analysis is utilized to examine the influence of
the tested variables on the transformation of the
agricultural structure in Sumatra Island. The
research data is in the form of panel data, combining
time series data (temporal sequence) with cross-
sectional data (cross-sectional data) [11], assisted by
Microsoft Excel and E-views software. The
equation employed in this study is:
Yit = β0 + β1X1 it + β2X2 it + β3X3 it + β4X4 it + β5X5 it + β6X6 it + eit
Keterangan:
Y = Economic Agriculture Shift
B0 = Constanta
B1,2,3 = Regression coefficient
X1 = Unemployment
X2 = Poverty
X3 = Pulation Density
X4 = Investment
X5 = Expections School
i = Cross section
t = Time series
e = Error
In panel data analysis, several steps are undertaken:
1. Model Estimation This involves determining
the model estimation based on various models
such as the Common Effect Model (CEM),
Fixed Effect Model (FEM), and Random Effect
Model (REM) [12].
2. Model Selection The selection of the best
model is determined through various tests,
including the Chow Test [13], Hausman Test,
and Breusch-Pagan Test.
3. Once the model is chosen, it is then examined
for potential issues such as multicollinearity
[14] and Heteroskedasticity [15].
4. Regression testing using the selected model.
3 General Description
Sumatra Island is the fourth-largest island in the
world, situated in Indonesia, featuring diverse
geography encompassing mountains, lowlands, and
beautiful coastlines. In Indonesia, Sumatra Island
holds several advantages that make it exceptional,
including vast tropical rainforests and rare wildlife
such as tigers, elephants, and Sumatran orangutans.
The island plays a crucial role in the national
economy as a significant center for large-scale
production of palm oil and rubber. The rich cultural
diversity, with ethnic groups like the Minangkabau,
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.17
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Batak, Aceh, and Melayu, contributes significantly
to Indonesia's cultural diversity. Furthermore,
Sumatra offers various stunning tourist destinations
with natural beauty that remains relatively
untouched. Despite its numerous advantages, the
island faces challenges such as deforestation and
environmental issues. Therefore, maintaining a
balance between natural resource utilization and
environmental protection is crucial for Sumatra's
future.
Sumatra Island comprises 10 provinces, covering an
area of 473,481 km2, and has a substantial
population of 61,617,515 people as of 2022 [16].
The Sumatra region holds a strategic location within
the national, ASEAN regional, and global
frameworks. Nationally, Sumatra is a hub for the
production and processing of various agricultural
products such as rubber and palm oil, as well as
being a significant contributor to the mining sector
[17].
Figure 1. Sumatera Island Map
4 Result and Discussion
4.1 Shift in Agricultural Sector
Economic development is often associated with an
increase in economic growth supported by changes
in more modern sectors. A region undergoing
economic development always faces issues related
to income distribution. However, successful
economic development in a region is typically
accompanied by increased economic growth [18].
The economic growth in Sumatra Island can be
observed in Figure 2.
Figure 2. Economic growth of Sumatera Island
Based on Figure 2, it can be observed that economic
growth in Sumatra Island fluctuates during the
period of 2011-2022. Declines occur in every 5-year
period, marked by a decrease in economic growth in
2015 and 2022. In 2015, there was a decrease in
economic growth from 4.39 percent, dropping to
2.92 percent. However, this decline in growth then
rose to 4.34 percent, similar to the rate in 2014, and
later fell to a negative value of -1.21 in 2020. This
was due to the COVID-19 pandemic, causing
significant impacts on economic growth, with
reduced economic activities, losses in the tourism
and travel sector, disruptions in the supply chain,
decreased investment, job losses, and high
uncertainty. Many countries responded to the
pandemic with economic stimulus measures, but
economic recovery takes time. Changes in
consumption patterns and business sector
adaptations have occurred in response to the
evolving pandemic situation. The impacts of the
pandemic will continue to be felt for a longer period
[19].
Industrialization in Sumatra Island is still in a
balanced condition as the primary sector (agriculture
and mining) continues to contribute more compared
to the secondary or tertiary sectors. This is because
if the tertiary sector surpasses the industrial sector, it
may lead to an imbalance in economic growth,
dependence on the service sector, potential imports
due to inadequate raw materials within the region,
changes in job availability, and more. However, a
balanced increase in the tertiary sector alongside the
primary and secondary sectors is more advantageous
as it provides more diverse employment
opportunities in this sector [20]. This contrasts with
the findings of another study [21], where the
secondary sector becomes the largest contributor,
followed by the tertiary sector, and the rest is the
primary sector [21]. The agricultural sector remains
a primary focus in Sumatra Island as it contributes
the most to economic growth, albeit with fluctuating
values.
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.17
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Teguh Endaryanto, Muhammad Irfan Affandi, Firdasari
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Sumatra Island continues to be the backbone of
agriculture in Indonesia [22]. Sumatra and
Kalimantan are islands that play a significant role in
fulfilling food needs and agricultural exports in
Indonesia. This underscores Sumatra Island's crucial
role in supporting agriculture, and any shift or slight
decrease in its contribution will impact agriculture
nationally. The average contribution of the
agricultural sector in Sumatra Island can be seen in
Figure 3
Figure 3. Agricultural Contribution Sector in
Sumatera Island.
Based on Figure 3, it can be seen that the
contribution of the agricultural sector undergoes
significant fluctuations during the period of 2010-
2022, with the lowest contribution in 2019, where it
dropped to only 22.54 percent. Leading sectors that
contribute the most in Sumatra Island are often
hindered by factors impeding economic growth,
such as electricity, illegal levies, road quality, and a
lack of supporting market facilities.
These factors, if addressed, could actually support
commercial agriculture. Differences in contributions
each year lead to a shift in percentage contributions,
resulting in significant numerical differences. A
large contribution does not guarantee continuous
positive growth for agriculture. Based on
contributions from 2010-2022, there is a decrease of
0.624 percent in the agricultural sector's
contribution, placing it among the sectors
experiencing a decreased contribution shift.
Together with the mining sector, which decreased
by 6.11 percent, and mandatory government
administration, defense, and social security, which
decreased by 0.060 percent.
The calculated shift using the initial year (2010) and
the final year (2022) results in a mostly negative
shift value in the agricultural sector. This indicates a
tendency in Sumatra Island for the agricultural
sector to shift towards other sectors. [23] [24] state
that within a period of five to ten years, the
agricultural sector decreases very little, and its
contribution shifts to the secondary and tertiary
sectors, such as accommodation and food services,
trade in services, and service sectors. It is evident
that the decrease in the agricultural sector's shift is
followed by a positive shift in the wholesale and
retail trade sector; Repair of motor vehicles and
motorcycles by 1.826 percent, the information and
communication sector by 1.347 percent, and the
construction sector with a positive shift of 1.335
percent.
However, as a primary sector, agriculture has not
undergone significant changes, as shown in Table 1.
Table 1. Economic Contribution in Sumatera Island
Sumatra Island positions agriculture as the most
contributing sector, but it appears that this sector has
not transformed significantly over the 12-year
period (2010-2022). During this time,
transformation is essential for the primary sector to
support income by adding value to primary products
through the tertiary and secondary sectors.
4.2 Factors Influencing the Agricultural Sector
Factors influencing the shift in the economic
agricultural sector were analyzed using panel data
regression analysis with the assistance of Eviews 12
software. The aim was to analyze the factors
influencing the economic shift, particularly in the
agricultural sector in Sumatra Island during the
period 2010-2022. The dependent variable used in
this study is the transformation depicted by the
value of the Gross Regional Domestic Product at
constant prices for the agricultural sector (Y). The
independent variables suspected to affect the
economic shift in the agricultural sector are the
Open Unemployment Rate (X1), Population Density
contribution shift
Sumatera
A. Agriculture, Forestry, and Fisheries -0.62
B. Mining and Quarrying -6.11
Primary Sector -6.73
C. Mmanufacturing Industry 0.41
D. Electricity and Gas Supply 0.04
E. Water Supply, Waste Management, and Recycling 0.00
F. construction 1.33
Secondary Sector 1.79
G. Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles 1.83
H. Transportation and Storage 0.26
I. Accommodation and Food and Beverage Service Activities 0.26
J. Information and Communication 1.35
K. Financial and Insurance Activities 0.18
L. Real Es tate 0.40
M,N. Professional, Scientific, and Technical Activities 0.05
O. Public Administration, Defense, and Mandatory Social Security -0.06
P. Education Services 0.31
Q. Health and Social Work Activities 0.27
R,S,T,U. Other Services 0.09
Tertiary Sector 4.94
Sector
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DOI: 10.37394/232033.2024.2.17
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(X2), Number of Poor People (X3), Investment
(X4), and Years of Schooling Expectancy (X5).
4.2.1 Determining the best model
1. Chow Test
The Chow Test is a test aimed at selecting the best
estimation model that can be used for panel data
research. The hypotheses used in this test are: H0:
Common Effect Model (CEM) H1: Fixed Effect
Model (FEM). The detailed results of the analysis
using the Chow test estimation method can be seen
in the table.
Table 2. Chow Test Result
It can be seen in Table that the results of the Chow
Test indicate that the obtained probability is 0.0001,
and this value is less than the significance level
(0.005). Therefore, the best estimation model used is
the Fixed Effect Model (FEM).
2. Hausman Test
The Hausman Test is a test used to choose between
the Random Effect Model (REM) or the Fixed
Effect Model (FEM). After being tested with the
Chow Test, and the selected model is FEM, it must
then be tested using the Hausman Test to assess
whether it is the best estimation model. The
hypotheses used are:
H0: Random Effect Model (REM)
H1: Fixed Effect Model (FEM)
The detailed results of the analysis using the
Hausman Test can be seen in Table 3
Table 3, Hausman Test Result
Berdasarkan hasil Hausman Test yang dapat dilihat
pada diketahui bahwa nilai probabilitas yang
dihasilkan adalah 0,0034 yang berarti nilai tersebut
kurang dari taraf signifikan (0,005) oleh karena itu,
model terbaik yang digunakan adalah Fixed Effect
Model (FEM).
3. Lagrange Multiplier
The Lagrange Multiplier test is used to choose
between the Random Effect Model (REM) and
Common Effect Model (CEM). However, since the
selected model from the Hausman Test is the Fixed
Effect Model (FEM), the Lagrange Multiplier test is
not necessary.
4.2.2 Classical Assumption Tests
Panel data allows for a more comprehensive study
of behaviors within a model, eliminating the need
for classic assumption tests [25]. However,
according to [26], classic assumption tests
commonly used in linear regression with the
Ordinary Least Squared (OLS) approach include
Linearity, Autocorrelation, Heteroskedasticity,
Multicollinearity, and Normality. Nevertheless, not
all classic assumption tests need to be conducted for
every linear regression model with the OLS
approach; only multicollinearity and
heteroskedasticity are essential.
1. Multicollinearity
Multicollinearity occurs when there is a correlation
between independent variables in a research dataset,
meaning that variables X1, X2, X3, X4, and X5 are
correlated and interrelated. The Multicollinearity
test can be conducted by examining the Variance
Inflation Factor (VIF) values in the data. The
criteria for the VIF test are as follows:
1. If the VIF value < 10, there is no
multicollinearity.
2. If the VIF value > 10, there is multicollinearity.
The results of the multicollinearity test can be seen
in Table 4.
Table 4. Multicollinearity Result
No
VIF
1
1.415
2
1.767
3
1.667
4
1.406
5
2.190
Based on Table 25, the VIF values are less than 10
(VIF < 10), indicating that there is no
multicollinearity in the data. Thus, it can be
concluded that the data is normally distributed as
there is no correlation between independent
variables.
Effects Test Statistic d.f. Prob.
Cross-section F 321.779524 (9,105) 0.0000
Cross-section Chi-square 402.329488 9 0.0000
Correlated Random Effects - Hausman Test
Equation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 17.673545 5 0.0034
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.17
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2. Heteroskedasticity
The Heteroskedasticity test aims to examine
whether there is a variance difference from one
observation to another in a regression model. If the
variance of residuals from one observation to
another remains the same, it is called
homoskedasticity; if it differs, it is called
heteroskedasticity [25]. In this observation, the test
for heteroskedasticity used is the Glejser Test. The
criteria for the Glejser Test are as follows:
1. If the sig value < 0.05, there is
heteroskedasticity.
2. If the sig value ≥ 0.05, there is no
heteroskedasticity.
The results of the heteroskedasticity test using
Eviews 12 software can be seen in Table 5.
Table 5. Heteroskedasticity Result
Based on the heteroskedasticity test, it can be
determined that all probabilities of the independent
variables have values > 0.05, indicating no
heteroskedasticity in the research data.
4.2.3 Interpretation of the Best Model
The panel data test in this study resulted in the
following equation:
After passing the multicollinearity and
heteroskedasticity tests, the data output from the
FEM estimation can be interpreted. The results of
the best model (FEM) output estimation data can be
seen in Table 6.
Table 6. Data Panel Result
Variable
Coefficient
Std. Error
t-statistic
Prob.
C
832.1709
1979.507
0.420393
0.6751
X1
-111.8234
49.36492
-2.265240
0.0256
X2
-2.952834
8.261532
-0.357420
0.7215
X3
-29.39625
14.43857
-2.035952
0.0443
X4
0.817769
0.107402
7.614098
0.0001
X5
735.2492
231.0607
3.182060
0.0019
F-statistic
Prob(F-statistic)
0.000000
R-squared
0.987662
Adjusted Rr-Squared
0.986017
1. Coefficient of Determination
The coefficient of determination results, as seen in
Table 27, is 0.987662. This means that 98.76% of
the economic shift in the agricultural sector is
influenced by Unemployment Rate (X1), Number of
Poor Population (X3), Investment (X4), and Years
of Schooling (X5). The remaining 1.24% is
explained by other variables not included in the
model.
2. Simultant Test (F-Test)
The F-Test is conducted to determine how the
independent variables (X) jointly affect the
dependent variable (Y). Collectively, the
independent variables - Unemployment Rate (X1),
Population Density (X2), Number of Poor
Population (X3), Investment (X4), and Years of
Schooling (X5) - significantly influence the shift in
the agricultural sector's economy with a significance
level of 0.0001
3. Partial Test (T-Test)
There are four significant factors: Unemployment
Rate (X1), Number of Poor Population (X3),
Investment (X4), and Years of Schooling (X5).
The variable Unemployment Rate significantly and
negatively affects the shift in the economic
structure, specifically in the agricultural sector. At a
confidence level of 95%, an increase of 1% in the
unemployment rate will shift the agricultural sector
negatively by 111.8234 billion IDR [27]. This
indicates that an increase in unemployment will
impact the macroeconomic value, especially in the
agricultural sector, in a negative direction. This is
consistent with the statements of [28] and [29],
which state that the agricultural sector and
unemployment have a negative impact.
The variable Number of Poor Population
significantly and negatively affects the shift in the
economic structure, specifically in the agricultural
sector. At a confidence level of 95%, an increase of
1 person in the number of poor population will shift
the agricultural sector negatively by 29.39625
billion IDR. This indicates that an increase in the
number of poor population will impact the
macroeconomic value, especially in the agricultural
sector. This is consistent with [30] [31], stating that
the agricultural sector has a negative impact on
poverty. This is because the agricultural sector, as
Variable Coefficient Std. Error t-Statistic Prob.
C 17528.78 10359.63 1.692027 0.0936
X1 500.3026 261.4075 1.913880 0.0584
X2 17.44213 441.0979 0.039543 0.9685
X3 -1436.041 766.7479 -1.872898 0.0639
X4 0.136943 0.566012 0.241943 0.8093
X5 -10159.41 12123.73 -0.837978 0.4039
Yit = 832.1709 - 111.8234X1it - 2.952834X2it - 29.39625X3it + 0.817769X4it + 735.2492X5it + Eit
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.17
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Teguh Endaryanto, Muhammad Irfan Affandi, Firdasari
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Volume 2, 2024
the largest contributor, can create job opportunities
and reduce poverty [30].
The variable Investment significantly and positively
affects the shift in the economic structure,
specifically in the agricultural sector. At a
confidence level of 99%, an increase of 1% in
investment will shift the agricultural sector
positively by 0.817769 billion IDR. This is
consistent with research [32] stating that investment
has a positive impact on the agricultural sector.
The variable Years of Schooling significantly and
positively affects the shift in the economic structure,
specifically in the agricultural sector. At a
confidence level of 99%, an increase of 1 year in
schooling will shift the agricultural sector positively
by 735.2492 billion IDR. This is consistent with
[33] stating that years of schooling will increase the
human development index through adequate and
sufficient education.
4 Conclusion
Based on Figure 3, it can be seen that the
agricultural sector contributes the most to the Gross
Regional Domestic Product (GDP) of Sumatra
Island. However, this sector has not undergone
significant changes, indicating that the agricultural
sector has not transformed from a traditional
primary sector to a more modern sector with the
leading sector being the manufacturing industry,
during the 12-year period. As seen in Table 4, the
manufacturing industry only experienced a
contribution increase of 0.41, a value even smaller
than the tertiary sector. The small shift in the
contribution of the agricultural sector is influenced
by several variables: unemployment rate, poverty
rate, investment, and years of schooling. All these
variables have a significant impact both
simultaneously and partially on the agricultural
sector in Sumatra Island.
References:
[1] Edon,T.J. Identifikasi Sektor Unggulan di Kota
Salatiga Periode 2010-2016, Jurnal Ilmu Sosial
dan Humaniora. Vol 8, No 2, 2019, pp. 1122-
131.
[2] Hutapea, Koleangan and Rorong, Analisis
Sektor Basis dan NonBasis Serta Daya Saing
Ekonomi dalam Peningkatan Pertumbuhan
Ekonomi Kota Medan, Jurnal Berkala Ilmiah
Efisiensi, Vol 20, No 3, 2020, pp. 1-11.
[3] Badan Pusat Statistik, 2023. PDRB Triwulan
Atas Dasar Harga Konstan Menurut Lapangan
Usaha di Provinsi Seluruh Indonesia (Miliar
Rupiah).[Online]
https://www.bps.go.id/statictable/2022/09/02/2
206/-seri-2010-pdrb-triwulanan-atas-dasar-
harga-konstan-menurut-lapangan-usaha-di-
provinsi-seluruh-indonesia-miliar-rupiah-2010-
2023.html
[4] Suryani, N, Budiman. C., Hidayat, R, Pemetaan
Komoditi Unggulan Sektor Pertanian di
Provinsi Sumatera Barat, JOSETA,Vol 1, No 2,
2019,pp. 120-129.
[5] Arsyad. L, Ekonomi Pembangunan Edisi
Keempat, STIE YKPN, 2004.
[6] Sugiyono, Metode Penelitian Kuantitatif.
Kualitatif, dan R&D, Alfabeta, 2011.
[7] Moleong, L.J, Metode Penelitian Kualitatif. PT
Remaja Rosadakarya, 2017.
[8] Arifianto, S, Implementasi Metode Penelitian
“Studi Kasus” Dengan Pendekatan Kualitatif.,
Aswaja Pressindo, 2016.
[9] Sugiyono, Metode Penelitian Kuantitatif.
Kualitatif, dan R&D, Alfabeta, 2017
[10] Sugiyono, Metode Penelitian Kuantitatif.
Kualitatif, dan R&D, Alfabeta, 2009.
[11] Hsiao, Analysis of Panel Data. Cambridge
university press, 2014.
[12] Widarjono, A, Ekonometrika Pengantar dan
Aplikasinya, Ekonosia, 2009.
[13] Ansofino, Jolianis, Yolamalinda, and H,A,
Buku Ajar Ekonometrika, Deepublish, 2016.
[14] Firdaus, M, Aplikasi Ekonometrika untuk Data
Panel dan Time Series, Penerbit IPB Press,
2011.
[15] Madany, N., Ruliana, and Rais, Z., Regresi
Data Panel dan Aplikasinya dalam Kinerja
Keuangan terhadap Pertumbuhan Laba
Perusahaan Idx Lq45 Bursa Efek Indonesia,
Variasi, Vol 4, No 2, 2022, pp. 79-94.
[16] Badan Pusat Statistik, 2022. Jumlah Penduduk
Menurut Provinsi (Ribu Jiwa), 2020-2022.
[Online]https://sumsel.bps.go.id/indicator/12/5
73/1/jumlah-penduduk-menurut-provinsi.html
[17] Badan Pusat Statistik, Statistik Hortikultura,
BPS, 2022.
[18] Rajab, A and Kamarudin, J, Analisis
Pertumbuhan Ekonomi, Ketimpangan Wilayah
dan Tingkat Kemiskinan, Forum Ekonomi, Vol
23, No 4, 2021, pp. 607-613.
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.17
Dwi Haryono, Vinni Aurelia Salsabila,
Teguh Endaryanto, Muhammad Irfan Affandi, Firdasari
E-ISSN: 2945-1159
201
Volume 2, 2024
[19] Zulkipli and Muharir, Dampak Covid-19
Terhadap Perekonomian di Indonesia, Jimesha,
Vol 1, No 1, 2021, pp. 7-12.
[20] Alfarabi, M.A., Hidayat, M.S., and Rahmadi,
S., Perubahan Struktur Ekonomi dan
Dampaknya Terhadap Kemiskinan di Provinsi
Jambi, Perspektif Pembiayaan dan
Pembangunan Daerah, Vol 1, No 3, 2014, pp.
171-178.
[21] Hasanah, F., Setiawan, I., Noor, T., and Yudha,
E.P., Analisis Potensi Sektor Unggulan dan
Perubahan Struktur Ekonomi di Kabupaten
Serang Provinsi Banten, Mimbar Agribisnis,
Vol 7, No 1, 2021, pp. 947-960.
[22] Bank Indonesia, Laporan Perekonomian
Indonesia, Sinergi dan Inovasi Memperkuat
Ketahanan dan Kebangkitan Menuju Indonesia
Maju, Bank Indonesia, 2023.
[23] Kurniawan, A, and Makarim, H., Perbedaan
Pergeseran Kontribusi Sektoral Terhadap
PDRB Menurut Kabupaten/Kota pada Masa
Pandemi Covid 19 di Provinsi Jawa Tengah,
Jurnal Geografi, Vol 19, No 1, 2022, pp. 1-9.
[24] Artika, I., Kencana, S., and Marini, I.,
Pergeseran Lapangan Usaha Sektor Pertanian,
Pertumbuhan Ekonomi dan Penurunan Tingkat
Kemiskinan di Provinsi Nusa Tenggara Barat,
Gara, Vol 12, No 1, pp. 111-117.
[25] Gujarati, N.D., Dasar-Dasar Ekonometrika.
Salemba, 2012.
[26] Basuki, A. T., and Prawoto, N, Analisis Regresi
dalam Penelitian Ekonomi dan Bisnis:
Dilengkapi Aplikasi SPSS dan Eviews,
Rajawali Press, 2016.
[27] Ronaldo, R, Pengaruh Inflasi dan Tingkat
Pengangguran terhadap Pertumbuhan Ekonomi
Makro di Indonesia, Jurnal Ekonomi, Vol 27,
No 3, pp. 137-144.
[28] Padang, L, and Murtala, Pengaruh Jumlah
Penduduk Miskin dan Tingkat Pengangguran
Terbuka Terhadap Pertumbuhan Ekonomi di
Indonesia, Jurnal Ekonomika Indonesia, Vol 8,
No 2, 2019, pp. 9-14.
[29] Fitri, I. F. and Satrio, I, Analisis Hubungan
Pertumbuhan Pertanian Terhadap
Pengangguran di Indonesia, Jurnal Sosial
Ekonomi dan Kebijakan Pertanian, Vol 8, No
1, 2019, pp. 1-6.
[30] Rohmat, N and Indrawati, L.R, Pengaruh
Sektor Pertanian, Industri Pengolahan dan
Pariwisata terhadap Kemiskinan di Jawa
Tengah Tahun 2016-2020, Jurnal Jendela
Inovasi Daerah, Vol 5, No 1, 2022, pp. 71-87.
[31] Niara, A and Zulfa, A, Pengaruh Kontribusi
Sektor Pertanian dan Industri Terhadap
Kemiskinan di Kabupaten Aceh Utara, Jurnal
Ekonomi Regional Unimal, Vol 2, No 1, 2019,
pp.28-37.
[32] Safri, S, Busar, A, and, San Noor, A, Pengaruh
investasi sektor pertanian dan tenaga kerja
terhadap produk domestik regional bruto
(PDRB)di Kabupaten Kutai
KartanegaraPeriode 2007-2018, Jurnal Ilmu
Ekonomi Mulawarman, Vol 6, No 2, 2021, pp.
1-10.
[33] Sabrina, R, Manurung A, and Sirait B,
Peningkatan Rata-Rata Lama Sekolah (RLS)
dari Harapan Lama Sekolah (HLS) di Sumatera
Utara, Jurnal Pendidikan Tambusai, Vol 6, No
1, 2022, pp. 4784-4792.
Contribution of Iindividual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Dwi Haryono and Teguh Endaryanto, responsible
for reviewing the literature. Firdasari and
Muhammad Irfan Affandi completed the write up of
thisresearch. Vinni Aurelia Salsabila, did the
empirical analysis of this study.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The research in this manuscript is supported by
Faculty of Agriculture, Universitas Lampung.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.17
Dwi Haryono, Vinni Aurelia Salsabila,
Teguh Endaryanto, Muhammad Irfan Affandi, Firdasari
E-ISSN: 2945-1159
202
Volume 2, 2024